{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、文本判别分类\n",
    "\n",
    "\n",
    "## 1、朴素贝叶斯\n",
    "\n",
    "\n",
    "### 1.1、拼写检查器\n",
    "\n",
    "    编辑距离"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re, collections\n",
    "\n",
    "def words(text):\n",
    "    return re.findall('[a-z]+', text.lower())\n",
    "\n",
    "def train(features):\n",
    "    model = collections.defaultdict(lambda: 1)# model里面没有存在的词默认都返回1\n",
    "    for f in features:\n",
    "        model[f] += 1\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NWORDS = train(words(open('./data/III/big.txt').read()))\n",
    "alphabet = 'abcdefghijklmnopqrstuvwxyz'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 编辑距离为1的集合\n",
    "def edits1(word):\n",
    "    n = len(word)\n",
    "    return  set([word[0:i]+word[i+1:] for i in range(n)] +                             # deletion\n",
    "             [word[0:i]+word[i+1]+word[i]+word[i+2:] for i in range(n-1)] +      # transposition\n",
    "             [word[0:i]+c+word[i+1:] for i in range(n) for c in alphabet] +       # alteration\n",
    "             [word[0:i]+c+word[i:] for i in range(n+1) for c in alphabet])         # insertion\n",
    "\n",
    "\n",
    "# 编辑距离为2的集合\n",
    "# 在这些编辑距离小于2的词中间，只把那些正确的词作为候选词\n",
    "def edits2(word):\n",
    "    return set(e2 for e1 in edits1(word) for e2 in edits1(e1) if e2 in NWORDS)\n",
    "\n",
    "\n",
    "# 判断该单词是不是在该文件中，如果在返回出来\n",
    "def known(words):\n",
    "    return set(w for w in words if w in NWORDS)\n",
    "\n",
    "\n",
    "# 拼写检查\n",
    "def correct(word):\n",
    "    # 编辑距离为0的优先，>1，>2 \n",
    "    candidates = known([word]) or known(edits1(word)) or known(edits2(word)) or [word]\n",
    "    return max(candidates, key=lambda w: NWORDS[w])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'love'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 测试：appl、tess、morw、appla\n",
    "correct(\"lovv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2、垃圾邮件检测\n",
    "\n",
    "    数学，没有实例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3、贝叶斯分类器\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4995</th>\n",
       "      <td>[天气, 炎热, 补水, 变得, 美国, 跑步, 世界, 杂志, 报道, 喝水, 身体, 补...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4996</th>\n",
       "      <td>[不想, 说, 话, 刺激, 说, 做, 只能, 走, 离开, 伤心地, 想起, 一句, 话...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4997</th>\n",
       "      <td>[岁, 刘晓庆, 最新, 嫩照, Ｏ, 衷, 诘, 牧跸, 庆, 看不出, 岁, 秒杀, 刘...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4998</th>\n",
       "      <td>[导语, 做, 爸爸, 一种, 幸福, 无论是, 领养, 亲生, 更何况, 影视剧, 中, ...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4999</th>\n",
       "      <td>[全球, 最美, 女人, 合成图, 国, 整形外科, 教授, 李承哲, 国际, 学术, 杂志...</td>\n",
       "      <td>时尚</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         contents_clean label\n",
       "4995  [天气, 炎热, 补水, 变得, 美国, 跑步, 世界, 杂志, 报道, 喝水, 身体, 补...    时尚\n",
       "4996  [不想, 说, 话, 刺激, 说, 做, 只能, 走, 离开, 伤心地, 想起, 一句, 话...    时尚\n",
       "4997  [岁, 刘晓庆, 最新, 嫩照, Ｏ, 衷, 诘, 牧跸, 庆, 看不出, 岁, 秒杀, 刘...    时尚\n",
       "4998  [导语, 做, 爸爸, 一种, 幸福, 无论是, 领养, 亲生, 更何况, 影视剧, 中, ...    时尚\n",
       "4999  [全球, 最美, 女人, 合成图, 国, 整形外科, 教授, 李承哲, 国际, 学术, 杂志...    时尚"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据\n",
    "df_train = pd.DataFrame({'contents_clean':contents_clean,'label':df_news['category']})\n",
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 映射\n",
    "label_mapping = {\"汽车\": 1, \"财经\": 2, \"科技\": 3, \"健康\": 4, \"体育\":5, \"教育\": 6,\"文化\": 7,\"军事\": 8,\"娱乐\": 9,\"时尚\": 0}\n",
    "df_train['label'] = df_train['label'].map(label_mapping)\n",
    "\n",
    "# 构造训练集、测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(df_train['contents_clean'].values, df_train['label'].values, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=False, max_df=1.0, max_features=4000, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 语料词库\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "vec = CountVectorizer(analyzer='word', max_features=4000,  lowercase = False)\n",
    "\n",
    "words = []\n",
    "for line_index in range(len(x_train)):\n",
    "    try:\n",
    "        words.append(' '.join(x_train[line_index]))\n",
    "    except:\n",
    "        print (line_index,word_index)\n",
    "\n",
    "# 词袋向量\n",
    "vec.fit(words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.804"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "classifier = MultinomialNB()\n",
    "classifier.fit(vec.transform(words), y_train)\n",
    "\n",
    "# 测试效果\n",
    "test_words = []\n",
    "for line_index in range(len(x_test)):\n",
    "    try:\n",
    "        test_words.append(' '.join(x_test[line_index]))\n",
    "    except:\n",
    "         print (line_index,word_index)\n",
    "# test_words[0]\n",
    "classifier.score(vec.transform(test_words), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8152"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 换成TF-IDF向量特征\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(analyzer='word', max_features=4000,  lowercase = False)\n",
    "vectorizer.fit(words)\n",
    "\n",
    "classifier2 = MultinomialNB()\n",
    "classifier2.fit(vectorizer.transform(words), y_train)\n",
    "\n",
    "classifier2.score(vectorizer.transform(test_words), y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2、LDA主题模型\n",
    "\n",
    "\n",
    "### 2.1、新闻主题词\n",
    "\n",
    "    数据源：http://www.sogou.com/labs/resource/ca.php"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import jieba\n",
    "import gensim\n",
    "from gensim import corpora, models, similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apple/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: read_table is deprecated, use read_csv instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category</th>\n",
       "      <th>theme</th>\n",
       "      <th>URL</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>汽车</td>\n",
       "      <td>新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价</td>\n",
       "      <td>http://auto.data.people.com.cn/model_15782/</td>\n",
       "      <td>经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>汽车</td>\n",
       "      <td>９１８　Ｓｐｙｄｅｒ概念车</td>\n",
       "      <td>http://auto.data.people.com.cn/prdview_165423....</td>\n",
       "      <td>呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>汽车</td>\n",
       "      <td>日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5249...</td>\n",
       "      <td>ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>汽车</td>\n",
       "      <td>清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_6144...</td>\n",
       "      <td>清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>汽车</td>\n",
       "      <td>大众敞篷家族新成员　高尔夫敞篷版实拍</td>\n",
       "      <td>http://auto.data.people.com.cn/news/story_5686...</td>\n",
       "      <td>在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  category                                 theme  \\\n",
       "0       汽车  新辉腾　４．２　Ｖ８　４座加长Ｉｎｄｉｖｉｄｕａｌ版２０１１款　最新报价   \n",
       "1       汽车                         ９１８　Ｓｐｙｄｅｒ概念车   \n",
       "2       汽车              日内瓦亮相　ＭＩＮＩ性能版／概念车－１．６Ｔ引擎   \n",
       "3       汽车                清仓大甩卖一汽夏利Ｎ５威志Ｖ２低至３．３９万   \n",
       "4       汽车                    大众敞篷家族新成员　高尔夫敞篷版实拍   \n",
       "\n",
       "                                                 URL  \\\n",
       "0        http://auto.data.people.com.cn/model_15782/   \n",
       "1  http://auto.data.people.com.cn/prdview_165423....   \n",
       "2  http://auto.data.people.com.cn/news/story_5249...   \n",
       "3  http://auto.data.people.com.cn/news/story_6144...   \n",
       "4  http://auto.data.people.com.cn/news/story_5686...   \n",
       "\n",
       "                                             content  \n",
       "0  经销商　电话　试驾／订车Ｕ憬杭州滨江区江陵路１７８０号４００８－１１２２３３转５８６４＃保常...  \n",
       "1       呼叫热线　４００８－１００－３００　服务邮箱　ｋｆ＠ｐｅｏｐｌｅｄａｉｌｙ．ｃｏｍ．ｃｎ  \n",
       "2  ＭＩＮＩ品牌在二月曾经公布了最新的ＭＩＮＩ新概念车Ｃｌｕｂｖａｎ效果图，不过现在在日内瓦车展...  \n",
       "3  清仓大甩卖！一汽夏利Ｎ５、威志Ｖ２低至３．３９万＝日，启新中国一汽强势推出一汽夏利Ｎ５、威志...  \n",
       "4  在今年３月的日内瓦车展上，我们见到了高尔夫家族的新成员，高尔夫敞篷版，这款全新敞篷车受到了众...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_news = pd.read_table('./data/III/val.txt', names=['category','theme','URL','content'], encoding='utf-8', sep='\\t')\n",
    "df_news = df_news.dropna()\n",
    "df_news.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /var/folders/8h/6ccn1ffd1g926n836p53h4nr0000gn/T/jieba.cache\n",
      "Loading model cost 0.970 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>content_segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     content_segment\n",
       "0  [经销商, 　, 电话, 　, 试驾, ／, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, ...\n",
       "1  [呼叫, 热线, 　, ４, ０, ０, ８, －, １, ０, ０, －, ３, ０, ０...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 在, 二月, 曾经, 公布, 了, 最新, 的, Ｍ, Ｉ...\n",
       "3  [清仓, 大, 甩卖, ！, 一汽, 夏利, Ｎ, ５, 、, 威志, Ｖ, ２, 低至, ...\n",
       "4  [在, 今年, ３, 月, 的, 日内瓦, 车展, 上, ，, 我们, 见到, 了, 高尔夫..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 清洗文本\n",
    "# 1 分词\n",
    "content = df_news.content.values.tolist()\n",
    "content_cut = []\n",
    "for line in content:\n",
    "    current_segment = jieba.lcut(line)\n",
    "    if len(current_segment) > 1 and current_segment != '\\r\\n':\n",
    "        content_cut.append(current_segment)\n",
    "\n",
    "df_content = pd.DataFrame({'content_segment':content_cut})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contents_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      contents_clean\n",
       "0  [经销商, 电话, 试驾, 订车, Ｕ, 憬, 杭州, 滨江区, 江陵, 路, 号, 转, ...\n",
       "1  [呼叫, 热线, 服务, 邮箱, ｋ, ｆ, ｐ, ｅ, ｏ, ｐ, ｌ, ｅ, ｄ, ａ,...\n",
       "2  [Ｍ, Ｉ, Ｎ, Ｉ, 品牌, 二月, 公布, 最新, Ｍ, Ｉ, Ｎ, Ｉ, 新, 概念...\n",
       "3  [清仓, 甩卖, 一汽, 夏利, Ｎ, 威志, Ｖ, 低至, 万, 启新, 中国, 一汽, ...\n",
       "4  [日内瓦, 车展, 见到, 高尔夫, 家族, 新, 成员, 高尔夫, 敞篷版, 款, 全新,..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2 去除停用词\n",
    "stopwords = pd.read_csv(\"./data/III/stopwords.txt\", index_col=False, sep=\"\\t\", quoting=3, names=['stopword'], encoding='utf-8')\n",
    "\n",
    "# 方法一：\n",
    "def drop_stopwords(contents,stopwords):\n",
    "    contents_clean = []\n",
    "    all_words = []\n",
    "    for line in contents:\n",
    "        line_clean = []\n",
    "        for word in line:\n",
    "            if word in stopwords:\n",
    "                continue\n",
    "            line_clean.append(word)\n",
    "            all_words.append(str(word))\n",
    "        contents_clean.append(line_clean)\n",
    "    return contents_clean, all_words\n",
    "# print (contents_clean)\n",
    "\n",
    "# 方法二：\n",
    "# df_content.content_S.isin(stopwords.stopword)\n",
    "# df_content=df_content[~df_content.content_S.isin(stopwords.stopword)]\n",
    "\n",
    "# 处理\n",
    "contents = df_content.content_segment.values.tolist()\n",
    "stopwords = stopwords.stopword.values.tolist()\n",
    "contents_clean, all_words = drop_stopwords(contents, stopwords)\n",
    "\n",
    "df_content = pd.DataFrame({'contents_clean': contents_clean})\n",
    "df_content.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    LDA主题模型，要求list of list形式，分词好整个语料"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, '0.007*\"中\" + 0.006*\"观众\" + 0.005*\"中国\" + 0.005*\"电影\" + 0.004*\"Ｍ\"')\n",
      "(1, '0.011*\"男人\" + 0.005*\"孩子\" + 0.003*\"文化\" + 0.003*\"Ｍ\" + 0.003*\"中\"')\n",
      "(2, '0.006*\"万\" + 0.004*\"号\" + 0.003*\"转\" + 0.002*\"孩子\" + 0.002*\"中\"')\n",
      "(3, '0.027*\"ａ\" + 0.025*\"ｅ\" + 0.020*\"ｏ\" + 0.017*\"ｒ\" + 0.016*\"ｎ\"')\n",
      "(4, '0.006*\"球队\" + 0.005*\"中国\" + 0.005*\"选手\" + 0.005*\"中\" + 0.004*\"Ｓ\"')\n",
      "(5, '0.008*\"考生\" + 0.006*\"中国\" + 0.005*\"中\" + 0.003*\"恋情\" + 0.003*\"撒\"')\n",
      "(6, '0.009*\"中\" + 0.008*\"说\" + 0.004*\"女人\" + 0.003*\"男人\" + 0.003*\"比赛\"')\n",
      "(7, '0.005*\"中\" + 0.003*\"官兵\" + 0.003*\"市场\" + 0.002*\"万\" + 0.002*\"说\"')\n",
      "(8, '0.007*\"中\" + 0.004*\"说\" + 0.003*\"中国\" + 0.003*\"吃\" + 0.003*\"男人\"')\n",
      "(9, '0.005*\"中\" + 0.004*\"肌肤\" + 0.003*\"皮肤\" + 0.003*\"食物\" + 0.003*\"维生素\"')\n"
     ]
    }
   ],
   "source": [
    "# 做映射，相当于词袋\n",
    "dictionary = corpora.Dictionary(contents_clean)\n",
    "corpus = [dictionary.doc2bow(sentence) for sentence in contents_clean]\n",
    "\n",
    "# LDA模型\n",
    "lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=10)\n",
    "for topic in lda.print_topics(num_topics=10, num_words=5):\n",
    "    print (topic)\n",
    "\n",
    "# print (lda.print_topic(1, topn=5))# 单独打印一号分类结果"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apple/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:5: FutureWarning: using a dict on a Series for aggregation\n",
      "is deprecated and will be removed in a future version\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>all_words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4077</th>\n",
       "      <td>中</td>\n",
       "      <td>5199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4209</th>\n",
       "      <td>中国</td>\n",
       "      <td>3115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88255</th>\n",
       "      <td>说</td>\n",
       "      <td>3055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104747</th>\n",
       "      <td>Ｓ</td>\n",
       "      <td>2646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1373</th>\n",
       "      <td>万</td>\n",
       "      <td>2390</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       all_words  count\n",
       "4077           中   5199\n",
       "4209          中国   3115\n",
       "88255          说   3055\n",
       "104747         Ｓ   2646\n",
       "1373           万   2390"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算词频\n",
    "import numpy\n",
    "\n",
    "df_all_words = pd.DataFrame({'all_words': all_words})\n",
    "words_count = df_all_words.groupby(by=['all_words'])['all_words'].agg({\"count\": numpy.size})\n",
    "words_count = words_count.reset_index().sort_values(by=[\"count\"],ascending=False)\n",
    "words_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1a3d53deb8>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 词云\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (10.0, 5.0)\n",
    "\n",
    "wordcloud = WordCloud(font_path=\"./data/III/simhei.ttf\", background_color=\"white\", max_font_size=80)\n",
    "word_frequence = {x[0]:x[1] for x in words_count.head(100).values}\n",
    "wordcloud = wordcloud.fit_words(word_frequence)\n",
    "plt.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3、HMM隐马尔科夫\n",
    "\n",
    "\n",
    "### 3.1、给定模型参数的情况下\n",
    "\n",
    "   官网：http://hmmlearn.readthedocs.io/en/latest/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from hmmlearn import hmm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 隐藏状态：3个盒子\n",
    "states = [\"box 1\", \"box 2\", \"box3\"]\n",
    "n_states = len(states)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 观测状态：2种球\n",
    "observations = [\"red\", \"white\"]\n",
    "n_observations = len(observations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型参数\n",
    "\n",
    "初始状态\n",
    "隐藏状态转移矩阵\n",
    "隐藏状态-->观测状态转移矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_probability = np.array([0.2, 0.4, 0.4])\n",
    "\n",
    "transition_probability = np.array([\n",
    "  [0.5, 0.2, 0.3],\n",
    "  [0.3, 0.5, 0.2],\n",
    "  [0.2, 0.3, 0.5]\n",
    "])\n",
    "\n",
    "emission_probability = np.array([\n",
    "  [0.5, 0.5],\n",
    "  [0.4, 0.6],\n",
    "  [0.7, 0.3]\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用于离散观测状态\n",
    "model = hmm.MultinomialHMM(n_components=n_states)\n",
    "\n",
    "# 传入3个参数\n",
    "model.startprob_=start_probability\n",
    "model.transmat_=transition_probability\n",
    "model.emissionprob_=emission_probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 采用维特比算法，给定观测序列[0,1,0]\n",
    "seen = np.array([[0,1,0]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "logprob, box = model.decode(seen, algorithm=\"viterbi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['box3' 'box3' 'box3']\n"
     ]
    }
   ],
   "source": [
    "# 方法一\n",
    "print (np.array(states)[box])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['box3' 'box3' 'box3']\n"
     ]
    }
   ],
   "source": [
    "# 方法二\n",
    "box2 = model.predict(seen)\n",
    "print (np.array(states)[box2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-2.038545309915233\n"
     ]
    }
   ],
   "source": [
    "# 这个观测序列出现的概率值，取对数\n",
    "print (model.score(seen))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    得到观测序列的概率 ln0.13022≈−2.0385"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2、未给定模型参数的情况下\n",
    "\n",
    "    EM算法求解模型参数，tol : 停机阈值 n_iter : 最大迭代次数 n_components : 隐藏状态数目"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "startprob_ [2.78270783e-21 7.57676368e-33 1.00000000e+00]\n",
      "-----------------------------------------\n",
      "transmat_ [[3.17925815e-02 2.64330733e-02 9.41774345e-01]\n",
      " [3.36674126e-01 2.78785953e-01 3.84539921e-01]\n",
      " [4.53430785e-01 5.46569213e-01 1.50226779e-09]]\n",
      "-----------------------------------------\n",
      "emissionprob_ [[3.01747860e-01 6.98252140e-01]\n",
      " [4.35475250e-03 9.95645247e-01]\n",
      " [9.99983298e-01 1.67016762e-05]]\n",
      "-----------------------------------------\n",
      "score -6.2617135544799245\n"
     ]
    }
   ],
   "source": [
    "model2 = hmm.MultinomialHMM(n_components=n_states, n_iter=20, tol=0.01)\n",
    "X2 = np.array([[0,1,0,1],[0,0,0,1],[1,0,1,1]])\n",
    "model2.fit(X2)\n",
    "\n",
    "# 训练模型参数\n",
    "# pi、a、b\n",
    "print ('startprob_',model2.startprob_)\n",
    "print ('-----------------------------------------')\n",
    "print ('transmat_',model2.transmat_)\n",
    "print ('-----------------------------------------')\n",
    "print ('emissionprob_',model2.emissionprob_)\n",
    "print ('-----------------------------------------')\n",
    "print ('score',model2.score(X2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3、股票预测\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import datetime\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib.dates import YearLocator, MonthLocator\n",
    "from hmmlearn.hmm import GaussianHMM\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>low</th>\n",
       "      <th>high</th>\n",
       "      <th>volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-03-19</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>947.80</td>\n",
       "      <td>940.00</td>\n",
       "      <td>1105.0</td>\n",
       "      <td>63128585.34</td>\n",
       "      <td>1000.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-03-20</td>\n",
       "      <td>957.8</td>\n",
       "      <td>942.39</td>\n",
       "      <td>940.05</td>\n",
       "      <td>957.8</td>\n",
       "      <td>24674935.10</td>\n",
       "      <td>947.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-03-21</td>\n",
       "      <td>948.0</td>\n",
       "      <td>945.67</td>\n",
       "      <td>944.10</td>\n",
       "      <td>948.5</td>\n",
       "      <td>12035364.29</td>\n",
       "      <td>942.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-03-22</td>\n",
       "      <td>950.0</td>\n",
       "      <td>950.15</td>\n",
       "      <td>946.00</td>\n",
       "      <td>951.9</td>\n",
       "      <td>9859948.45</td>\n",
       "      <td>945.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-03-23</td>\n",
       "      <td>950.0</td>\n",
       "      <td>947.18</td>\n",
       "      <td>945.00</td>\n",
       "      <td>951.0</td>\n",
       "      <td>13198594.08</td>\n",
       "      <td>950.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    open   close     low    high       volume  Adj Close\n",
       "0 2007-03-19  1000.0  947.80  940.00  1105.0  63128585.34    1000.00\n",
       "1 2007-03-20   957.8  942.39  940.05   957.8  24674935.10     947.80\n",
       "2 2007-03-21   948.0  945.67  944.10   948.5  12035364.29     942.39\n",
       "3 2007-03-22   950.0  950.15  946.00   951.9   9859948.45     945.67\n",
       "4 2007-03-23   950.0  947.18  945.00   951.0  13198594.08     950.15"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ticker = \"gold\"\n",
    "start_date = datetime.date(2010, 1, 1)\n",
    "# end_date = datetime.date.today()\n",
    "end_date = datetime.date.today() - datetime.timedelta(days=15)\n",
    "\n",
    "\n",
    "data = pd.read_csv('./data/III/data2.csv', header=0)\n",
    "df = pd.DataFrame(data)\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-03-19</td>\n",
       "      <td>947.80</td>\n",
       "      <td>63128585.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-03-20</td>\n",
       "      <td>942.39</td>\n",
       "      <td>24674935.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-03-21</td>\n",
       "      <td>945.67</td>\n",
       "      <td>12035364.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-03-22</td>\n",
       "      <td>950.15</td>\n",
       "      <td>9859948.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-03-23</td>\n",
       "      <td>947.18</td>\n",
       "      <td>13198594.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date   close       volume\n",
       "0 2007-03-19  947.80  63128585.34\n",
       "1 2007-03-20  942.39  24674935.10\n",
       "2 2007-03-21  945.67  12035364.29\n",
       "3 2007-03-22  950.15   9859948.45\n",
       "4 2007-03-23  947.18  13198594.08"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True,drop=False)\n",
    "df.drop(['index','open','low','high','Adj Close'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>732754</td>\n",
       "      <td>947.80</td>\n",
       "      <td>63128585.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>732755</td>\n",
       "      <td>942.39</td>\n",
       "      <td>24674935.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>732756</td>\n",
       "      <td>945.67</td>\n",
       "      <td>12035364.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>732757</td>\n",
       "      <td>950.15</td>\n",
       "      <td>9859948.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>732758</td>\n",
       "      <td>947.18</td>\n",
       "      <td>13198594.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date   close       volume\n",
       "0  732754  947.80  63128585.34\n",
       "1  732755  942.39  24674935.10\n",
       "2  732756  945.67  12035364.29\n",
       "3  732757  950.15   9859948.45\n",
       "4  732758  947.18  13198594.08"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'] = df['date'].apply(datetime.datetime.toordinal)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = list(df.itertuples(index=False, name=None))\n",
    "dates = np.array([q[0] for q in df], dtype=int)\n",
    "close_v = np.array([q[1] for q in df])\n",
    "volume = np.array([q[2] for q in df])[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.41,  3.28,  4.48, -2.97, -6.47,  2.94,  3.25,  0.15, -2.23,\n",
       "       -4.44])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff = np.diff(close_v)\n",
    "diff[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([732755, 732756, 732757, 732758, 732761, 732763, 732764, 732765,\n",
       "       732711, 732739])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = dates[1:]\n",
    "dates[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2338,)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close_v = close_v[1:]\n",
    "close_v.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.column_stack([diff, volume])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.41000000e+00,  2.46749351e+07],\n",
       "       [ 3.28000000e+00,  1.20353643e+07],\n",
       "       [ 4.48000000e+00,  9.85994845e+06],\n",
       "       [-2.97000000e+00,  1.31985941e+07],\n",
       "       [-6.47000000e+00,  6.03949520e+06],\n",
       "       [ 2.94000000e+00,  4.43333641e+06],\n",
       "       [ 3.25000000e+00,  3.48799696e+06],\n",
       "       [ 1.50000000e-01,  2.61966938e+06],\n",
       "       [-2.23000000e+00,  1.83976186e+06],\n",
       "       [-4.44000000e+00,  1.70835469e+07]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting to HMM and decoding ...done\n",
      "Transition matrix - probability of going to any particular state\n",
      "[[0.94069834 0.00210767 0.05719399]\n",
      " [0.01743045 0.86197619 0.12059336]\n",
      " [0.07360904 0.02640655 0.89998441]]\n",
      "<bound method _BaseHMM.predict_proba of GaussianHMM(algorithm='viterbi', covariance_type='diag', covars_prior=0.01,\n",
      "      covars_weight=1, init_params='stmc', means_prior=0, means_weight=0,\n",
      "      min_covar=0.001, n_components=3, n_iter=1000, params='stmc',\n",
      "      random_state=None, startprob_prior=1.0, tol=0.01, transmat_prior=1.0,\n",
      "      verbose=False)>\n",
      "Means and vars of each hidden state\n"
     ]
    }
   ],
   "source": [
    "print(\"fitting to HMM and decoding ...\", end=\"\")\n",
    "# Make an HMM instance and execute fit\n",
    "model = GaussianHMM(n_components=3, covariance_type=\"diag\", n_iter=1000).fit(X)\n",
    "# Predict the optimal sequence of internal hidden state\n",
    "\n",
    "hidden_states = model.predict(X)\n",
    "print(\"done\")\n",
    "\n",
    "print(\"Transition matrix - probability of going to any particular state\")\n",
    "print(model.transmat_)\n",
    "print(model.predict_proba)\n",
    "\n",
    "print(\"Means and vars of each hidden state\")\n",
    "params = pd.DataFrame(columns=('State', 'Means', 'Variance'))\n",
    "for i in range(model.n_components):\n",
    "    params.loc[i] = [format(i), model.means_[i],np.diag(model.covars_[i])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>Means</th>\n",
       "      <th>Variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>[-0.42213236189395625, 27089802.727244433]</td>\n",
       "      <td>[218.01353649686692, 224001615414145.56]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[3.6331577626449048, 448216252.5353553]</td>\n",
       "      <td>[2626.772247491909, 3.3123868857806534e+17]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>[1.7186653988462637, 85254793.12420742]</td>\n",
       "      <td>[412.4976215774228, 1555431544004863.8]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  State                                       Means  \\\n",
       "0     0  [-0.42213236189395625, 27089802.727244433]   \n",
       "1     1     [3.6331577626449048, 448216252.5353553]   \n",
       "2     2     [1.7186653988462637, 85254793.12420742]   \n",
       "\n",
       "                                      Variance  \n",
       "0     [218.01353649686692, 224001615414145.56]  \n",
       "1  [2626.772247491909, 3.3123868857806534e+17]  \n",
       "2      [412.4976215774228, 1555431544004863.8]  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/apple/anaconda3/lib/python3.7/site-packages/pandas/plotting/_converter.py:129: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(model.n_components, sharex=True, sharey=True, figsize=(15,15))\n",
    "colours = matplotlib.cm.rainbow(np.linspace(0, 1, model.n_components))\n",
    "\n",
    "for i, (ax, colour) in enumerate(zip(axs, colours)):\n",
    "    # Use fancy indexing to plot data in each state.\n",
    "    mask = hidden_states == i\n",
    "    ax.plot_date(dates[mask], close_v[mask], \".\", c=colour)\n",
    "    ax.set_title(\"{0}th hidden state\".format(i))\n",
    "\n",
    "    # Format the ticks.\n",
    "    ax.xaxis.set_major_locator(YearLocator())\n",
    "    ax.xaxis.set_minor_locator(MonthLocator())\n",
    "\n",
    "    ax.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Returns        Volume\n",
      "0 -0.291144  3.130409e+07\n",
      "1  3.331597  3.971051e+08\n",
      "2  1.611638  9.055788e+07\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "expected_returns_and_volumes = np.dot(model.transmat_, model.means_)\n",
    "returns_and_volume_columnwise = list(zip(*expected_returns_and_volumes))\n",
    "expected_returns = returns_and_volume_columnwise[0]\n",
    "expected_volumes = returns_and_volume_columnwise[1]\n",
    "params = pd.concat([pd.Series(expected_returns), pd.Series(expected_volumes)], axis=1)\n",
    "params.columns= ['Returns', 'Volume']\n",
    "print (params)\n",
    "\n",
    "lastN = 7\n",
    "start_date = datetime.date.today() - datetime.timedelta(days=lastN*2) #even beyond N days\n",
    "end_date = datetime.date.today() \n",
    "\n",
    "dates = np.array([q[0] for q in df], dtype=int)\n",
    "\n",
    "predicted_prices = []\n",
    "predicted_dates = []\n",
    "predicted_volumes = []\n",
    "actual_volumes = []\n",
    "\n",
    "for idx in range(lastN):\n",
    "    state = hidden_states[-lastN+idx]\n",
    "    current_price = df[-lastN+idx][1]\n",
    "    volume = df[-lastN+idx][2]\n",
    "    actual_volumes.append(volume)\n",
    "    current_date = datetime.date.fromordinal(dates[-lastN+idx])\n",
    "    predicted_date = current_date + datetime.timedelta(days=1)\n",
    "    predicted_dates.append(predicted_date)\n",
    "    predicted_prices.append(current_price + expected_returns[state])\n",
    "    predicted_volumes.append(np.round(expected_volumes[state]))    \n",
    "\n",
    "#Returns\n",
    "plt.figure(figsize=(15, 5), dpi=100) \n",
    "plt.title(ticker, fontsize = 14)\n",
    "plt.plot(predicted_dates,close_v[-lastN:])\n",
    "plt.plot(predicted_dates,predicted_prices)\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.show()\n",
    "\n",
    "#Volumes\n",
    "plt.figure(figsize=(15, 5), dpi=100) \n",
    "plt.title(ticker, fontsize = 14)\n",
    "plt.plot(predicted_dates,actual_volumes)\n",
    "plt.plot(predicted_dates,predicted_volumes)\n",
    "plt.legend(['Actual','Predicted'])\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4、中文分词\n",
    "\n",
    "    看code III 中代码"
   ]
  }
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